Causal Language Modeling for Personalized Psychiatric Treatment Selection: Opportunities, Risks, and Clinical Translation
Main article
Abstract
Psychiatric treatment recommendation systems remain limited by low individual-level predictability when restricted to symptom scales, demographics and elementary physiological readouts. Despite extensive evidence of treatment-effect heterogeneity in mood, anxiety and psychotic disorders, conventional pipelines rarely translate average effects into reliable patient-specific recommendations. We argue that natural language—generated during clinical interviews and accumulated within electronic health records—constitutes a workflow-native, low-cost, and longitudinal substrate that, when processed through modern language-model encoders and combined with causal estimators of heterogeneous treatment effects, can meaningfully improve individualized predictability. This perspective synthesizes evidence from causal machine learning, clinical natural language processing, and digital phenotyping to propose a causal language modeling framework for personalized psychiatric treatment selection. We provide a hypothetical analysis comparing discrimination across illustrative scenarios, examine risks related to identification assumptions, distributional drift, fairness and privacy, and outline a staged roadmap for clinical translation that emphasises specification, validation and governance rather than algorithmic novelty alone.
